GenerateSimTask Class Reference

Inheritance diagram for GenerateSimTask:

Collaboration diagram for GenerateSimTask:

Public Member Functions
def	output_file_name (self, n_events=None, random_seed=None)
	Name of the ROOT output file with generated and simulated events. More...
def	output (self)
def	create_path (self)

Static Public Attributes
	n_events = b2luigi.IntParameter()
	Number of events to generate.
	experiment_number = b2luigi.IntParameter()
	Experiment number of the conditions database, e.g. More...
	random_seed = b2luigi.Parameter()
	Random basf2 seed. More...
	bkgfiles_dir
	Directory with overlay background root files. More...
string	queue = 'l'
	specify queue. More...

Detailed Description

Generate simulated Monte Carlo with background overlay.

Make sure to use different ``random_seed`` parameters for the training data
format the classifier trainings and for the test data for the respective
evaluation/validation tasks.

Definition at line 374 of file combined_quality_estimator_teacher.py.

Member Function Documentation

create_path()

def create_path

(

self

)

Create basf2 path to process with event generation and simulation.

Definition at line 417 of file combined_quality_estimator_teacher.py.

 
    def create_path(self):
        """
        Create basf2 path to process with event generation and simulation.
        """
        basf2.set_random_seed(self.random_seed)
        path = basf2.create_path()
        if self.experiment_number in [0, 1002, 1003]:
            runNo = 0
        else:
            runNo = 0
            raise ValueError(
                f"Simulating events with experiment number {self.experiment_number} is not implemented yet.")
        path.add_module(
            "EventInfoSetter", evtNumList=[self.n_events], runList=[runNo], expList=[self.experiment_number]
        )
        if "BBBAR" in self.random_seed:
            path.add_module("EvtGenInput")
        elif "V0BBBAR" in self.random_seed:
            path.add_module("EvtGenInput")
            path.add_module("InclusiveParticleChecker", particles=[310, 3122], includeConjugates=True)
        else:
            import generators as ge
            # WARNING: There are a few differences in the production of MC13a and b like the following lines
            # as well as ActivatePXD.. and the beamparams for bhabha... I use these from MC13b, not a... :/
            # import beamparameters as bp
            # beamparameters = bp.add_beamparameters(path, "Y4S")
            # beamparameters.param("covVertex", [(14.8e-4)**2, (1.5e-4)**2, (360e-4)**2])
            if "V0STUDY" in self.random_seed:
                if "V0STUDYKS" in self.random_seed:
                    # Bianca looked at the Ks dists and extracted these values:
                    mu = 0.5
                    beta = 0.2
                    pdgs = [310]  # Ks (has no antiparticle, Klong is different)
                if "V0STUDYL0" in self.random_seed:
                    # I just made the lambda values up, such that they peak at 0.35 and are slightly shifted to lower values
                    mu = 0.35
                    beta = 0.15  # if this is chosen higher, one needs to make sure not to get values >0 for 0
                    pdgs = [3122, -3122]  # Lambda0
                else:
                    # also these values are made up
                    mu = 0.43
                    beta = 0.18
                    pdgs = [310, 3122, -3122]  # Ks and Lambda0
                # create realistic momentum distribution
                myx = [i*0.01 for i in range(321)]
                myy = []
                for x in myx:
                    y = createV0momenta(x, mu, beta)
                    myy.append(y)
                polParams = myx + myy
                # define particles that are produced
                pdg_list = pdgs
 
                particlegun = basf2.register_module('ParticleGun')
                particlegun.param('pdgCodes', pdg_list)
                particlegun.param('nTracks', 8)  # number of particles (not tracks!) that is created in each event
                particlegun.param('momentumGeneration', 'polyline')
                particlegun.param('momentumParams', polParams)
                particlegun.param('thetaGeneration', 'uniformCos')
                particlegun.param('thetaParams', [17, 150])  # [0, 180]) #[17, 150]
                particlegun.param('phiGeneration', 'uniform')
                particlegun.param('phiParams', [0, 360])
                particlegun.param('vertexGeneration', 'fixed')
                particlegun.param('xVertexParams', [0])
                particlegun.param('yVertexParams', [0])
                particlegun.param('zVertexParams', [0])
                path.add_module(particlegun)
            if "BHABHA" in self.random_seed:
                ge.add_babayaganlo_generator(path=path, finalstate='ee', minenergy=0.15, minangle=10.0)
            elif "MUMU" in self.random_seed:
                ge.add_kkmc_generator(path=path, finalstate='mu+mu-')
            elif "YY" in self.random_seed:
                babayaganlo = basf2.register_module('BabayagaNLOInput')
                babayaganlo.param('FinalState', 'gg')
                babayaganlo.param('MaxAcollinearity', 180.0)
                babayaganlo.param('ScatteringAngleRange', [0., 180.])
                babayaganlo.param('FMax', 75000)
                babayaganlo.param('MinEnergy', 0.01)
                babayaganlo.param('Order', 'exp')
                babayaganlo.param('DebugEnergySpread', 0.01)
                babayaganlo.param('Epsilon', 0.00005)
                path.add_module(babayaganlo)
                generatorpreselection = basf2.register_module('GeneratorPreselection')
                generatorpreselection.param('nChargedMin', 0)
                generatorpreselection.param('nChargedMax', 999)
                generatorpreselection.param('MinChargedPt', 0.15)
                generatorpreselection.param('MinChargedTheta', 17.)
                generatorpreselection.param('MaxChargedTheta', 150.)
                generatorpreselection.param('nPhotonMin', 1)
                generatorpreselection.param('MinPhotonEnergy', 1.5)
                generatorpreselection.param('MinPhotonTheta', 15.0)
                generatorpreselection.param('MaxPhotonTheta', 165.0)
                generatorpreselection.param('applyInCMS', True)
                path.add_module(generatorpreselection)
                empty = basf2.create_path()
                generatorpreselection.if_value('!=11', empty)
            elif "EEEE" in self.random_seed:
                ge.add_aafh_generator(path=path, finalstate='e+e-e+e-', preselection=False)
            elif "EEMUMU" in self.random_seed:
                ge.add_aafh_generator(path=path, finalstate='e+e-mu+mu-', preselection=False)
            elif "TAUPAIR" in self.random_seed:
                ge.add_kkmc_generator(path, finalstate='tau+tau-')
            elif "DDBAR" in self.random_seed:
                ge.add_continuum_generator(path, finalstate='ddbar')
            elif "UUBAR" in self.random_seed:
                ge.add_continuum_generator(path, finalstate='uubar')
            elif "SSBAR" in self.random_seed:
                ge.add_continuum_generator(path, finalstate='ssbar')
            elif "CCBAR" in self.random_seed:
                ge.add_continuum_generator(path, finalstate='ccbar')
        # activate simulation of dead/masked pixel and reproduce detector gain, which will be
        # applied at reconstruction level when the data GT is present in the DB chain
        # path.add_module("ActivatePXDPixelMasker")
        # path.add_module("ActivatePXDGainCalibrator")
        bkg_files = background.get_background_files(self.bkgfiles_dir)
        if self.experiment_number == 1002:
            # remove KLM because of bug in background files with release 4
            components = ['PXD', 'SVD', 'CDC', 'ECL', 'TOP', 'ARICH', 'TRG']
        else:
            components = None
        simulation.add_simulation(path, bkgfiles=bkg_files, bkgOverlay=True, components=components)  # , usePXDDataReduction=False)
 
        path.add_module(
            "RootOutput",
            outputFileName=self.get_output_file_name(self.output_file_name()),
        )
        return path
 
 
# I don't use the default MergeTask or similar because they only work if every input file is called the same.

output()

def output

(

self

)

Generate list of output files that the task should produce.
The task is considered finished if and only if the outputs all exist.

Definition at line 410 of file combined_quality_estimator_teacher.py.

output_file_name()

def output_file_name	(		self,
			n_events = None,
			random_seed = None
	)

Name of the ROOT output file with generated and simulated events.

Create output file name depending on number of events and production
mode that is specified in the random_seed string.

Definition at line 399 of file combined_quality_estimator_teacher.py.

Member Data Documentation

bkgfiles_dir

bkgfiles_dir

static

Initial value:

=  b2luigi.Parameter(
        
 
    )

Directory with overlay background root files.

Definition at line 391 of file combined_quality_estimator_teacher.py.

experiment_number

experiment_number = b2luigi.IntParameter()

static

Experiment number of the conditions database, e.g.

defines simulation geometry

Definition at line 386 of file combined_quality_estimator_teacher.py.

queue

string queue = 'l'

static

specify queue.

E.g. choose between 'l' (long), 's' (short) or 'sx' (short, extra ram)

Definition at line 396 of file combined_quality_estimator_teacher.py.

random_seed

random_seed = b2luigi.Parameter()

static

Random basf2 seed.

It is further used to read of the production process to preserve clearness in the b2luigi output.

Definition at line 389 of file combined_quality_estimator_teacher.py.

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The documentation for this class was generated from the following file:

• tracking/scripts/tracking/train/combined_quality_estimator_teacher.py